Machine Learning Tutorial Python - 15: Naive Bayes Classifier Algorithm Part 2

In this python machine learning tutorial for beginners we will build email spam classifier using naive bayes algorithm. We will use sklearn CountVectorizer to convert email text into a matrix of numbers and then use sklearn MultinomialNB classifier to train our model. The model score
with this approach comes out to be very high (around 98%). Sklearn pipeline allows us to handle pre processing transformations easily with its convenient api. In the end there is an exercise where you need to classify sklearn wine dataset using naive bayes.

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Dataset: https://github.com/codebasics/py/blob/master/ML/14_naive_bayes

Exercise: https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/exercise.md

Code:https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/14_naive_bayes_2_email_spam_filter.ipynb

Exercise solution: https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/Exercise/14_naive_bayes_exercise.ipynb

Topics that are covered in this Video:

00:00 explore spam email dataset
02:33 sklearn CountVectorizer
04:30 types of naive bayes classifiers
05:23 sklearn MultinomialNB classifier
06:48 sklearn pipeline
09:35 Exercise

Next Video:
Machine Learning Tutorial Python - 16: Hyper parameter Tuning (GridSearchCV): https://www.youtube.com/watch?v=HdlDYng8g9s&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=17

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Видео Machine Learning Tutorial Python - 15: Naive Bayes Classifier Algorithm Part 2 канала codebasics